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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2022

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich data challenges existing data storage and access technologies, therefore precluding informatics advancements. Here we present MZA™ (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed by a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and efficient programmatic access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Supporting experimental data for "Conversion of Stored Thermochemical Potential into High Quality Heat in a Continuous Flow Reactor/Heat Exchanger"

Supporting experimental data for "Conversion of Stored Thermochemical Potential into High Quality Heat in a Continuous Flow Reactor/Heat Exchanger" including steady-state temperatures and conversion data.

Engineering↗

DOE OSTI2024

Exploring Ion Mobility Mass Spectrometry Data File Conversions to Leverage Existing Tools and Enable New Workflows

Ion mobility (IM) is often combined with LC-MS experiments to provide an additional dimension of separation for complex sample analysis. While highly complex samples are better characterized by the full dimensionality of LC-IM-MS experiments to uncover new information, downstream data analysis workflows are often not equipped to properly mine the additional IM dimension. For many samples the data acquisition benefits of including IM separations are all that is necessary to uncover sample information and the full dimensionality of the data is not required for data analysis. Post-acquisition reduction and adaptation of the dimensions of LC-IM-MS and IM-MS experiments into an LC-MS format opens the possibility to use a plethora of existing software tools. In this work, we developed data file conversion tools to reduce the complexity of IM data analysis. Three data file transformations are introduced in the PNNL PreProcessor software: 1) mapping the IM axis to the LC axis for IM-MS data, 2) converting the drift time vs. m/z space to CCS/z vs m/z space, and 3) transforming All Ions IM/MS mobility aligned fragmentation data to a standard LC-MS DDA data file format. Finally, these new data file conversions are demonstrated with corresponding lipidomics and proteomics workflows that leverage existing LC-MS data analysis software to highlight the benefits of the data transformations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Student Programs FY21 Conversion Report

Sandia National Labs has created a noteworthy and effective internship program whose focus is creating a talent pipeline for the laboratory. Our program utilizes industry standard conversion calculations to examine the effectiveness of the program and to compare to our competitors. Sandia defines students eligible for conversion as graduating in the given fiscal year and in their final degree program. Students indicate to SIP upon hire and at certain checkpoints throughout their internship if they are in their final degree program or not. This means that they will not continue to a higher degree program after they graduate. For instance, someone who is graduating with a master’s degree in the current FY and does not plan to pursue a PhD would be considered eligible, while an undergrad student who is graduating in the same year, but plans to pursue a graduate degree, would not be considered eligible for conversion. Conversion data pulled for this report includes all eligible interns for fiscal year 2021. We use a rolling population, which includes anyone who was an intern at some point during FY21. To calculate conversion, we narrow our population down to the students who graduated between October 2020 through September 2021, who have indicated that they are in their final degree program. The conversion data was pulled on 10/29/2021, so any conversions completed after this date will not be included in the calculation. Our conversion data includes students who separated from Sandia and returned as a staff member. Conversions also include FTE, LTE, and postdoc positions. We do not include conversion to contractor positions in our calculations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2026

Analog-to-digital converter based on voltage-controlled superconducting devices

The increasing demand for cryogenic electronics in superconducting and quantum computing systems calls for ultra-energy-efficient data conversion architectures that remain functional at deep cryogenic temperatures. Here, in this work, we present the first design of a voltage-controlled superconducting flash analog-to-digital converter (ADC) based on a voltage-controlled quantum-enhanced Josephson junction field-effect transistor (JJFET). Exploiting its strong gate tunability and transistor-like behavior, the JJFET offers a scalable alternative to conventional current-controlled superconducting devices while aligning naturally with CMOS-style design methodologies. Building on our previously developed Verilog-A compact model calibrated to experimental data, we design and simulate a three-bit JJFET-based flash ADC targeted for integration within cryogenic control and readout circuitry in quantum computing. The core comparator block is realized through careful bias current selection and augmented with a three-terminal nanocryotron to precisely define reference voltages. Cascaded JJFET comparators ensure robust voltage gain, cascadability, and logic-level restoration across stages. Simulation results demonstrate accurate quantization behavior with ultra-low power dissipation, underscoring the feasibility of voltage-driven superconducting mixed-signal circuits. This work establishes a critical step toward unifying superconducting logic and data conversion, paving the way for scalable cryogenic architectures in quantum–classical co-processors, low-power artificial intelligence accelerators, and next-generation energy-constrained computing platforms.

Analog-to-digital converter↗

DOE OSTI2023

System and method for high dynamic range waveform digitization

Diverse applications from particle physics experiments to lidar are driving cost and current reduction in giga-hertz sampling rate high-resolution data conversion. Multiple imagers captures a single pixel of data and require processing at very high speed. High-bandwidth high-rate signal sampling, analog-to-digital conversion, and transfer of large amounts of data to a digital data acquisition block are required in such systems. Dynamic range, power consumption, and transfer of high-speed, high-bit width data are key implementation challenges. Data acquisition architectures optimized for specific requirements of such systems may facilitate system implementation and reduce overall system cost.

Mostafanezhad, Isar↗

DOE OSTI2025

High-performance data format for scientific data storage and analysis

Here, in this article, we present the High-Performance Output (HiPO) data format developed at Jefferson Laboratory for storing and analyzing data from Nuclear Physics experiments. The format was designed to efficiently store large amounts of experimental data, utilizing modern fast compression algorithms. The purpose of this development was to provide organized data in the output, facilitating access to relevant information within the large data files. The HiPO data format has features that are suited for storing raw detector data, reconstruction data, and the final physics analysis data efficiently, eliminating the need to do data conversions through the lifecycle of experimental data. The HiPO data format is implemented in C++ and JAVA, and provides bindings to FORTRAN, Python, and Julia, providing users with the choice of data analysis frameworks to use. In this paper, we will present the general design and functionalities of the HiPO library and compare the performance of the library with more established data formats used in data analysis in High Energy and Nuclear Physics (such as ROOT and Parquete). In columnar data analysis, HiPO surpasses established data formats in performance and can be effectively applied to data analysis in other scientific fields.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data

To meet the demand for accuracy and real-time capability of PV system degradation evaluation, massive volume data is needed to run high-fidelity and high-efficiency simulations and perform advanced data analysis. However, PV farm operators have a series of difficulties with PV inverter data, such as data collection from multiple channels, massive data storage, data management and massive data analysis. To address these challenges, we developed an integrated data management platform capable of data acquisition, processing, storage, query, and performing big data analysis utilizing AI algorithms. The platform can also achieve data correctness verification and provide an effective distributed data management solution to retrieve massive data and establish a connection to distributed computational frameworks.

data management platform↗

DOE OSTI2023

Integrated Large-Scale Data Management Platform for Photovoltaic Power Conversion Equipment (PCE) Reliability Data: Preprint

data management↗

DOE OSTI2022

Optimizing feed modulation for coupled methane and NO x conversion over Pd-Pt/Mn 0.5 Fe 2.5 O 4 /Al 2 O 3 monolith catalyst

Here the impacts of feed modulation (frequency, amplitude) and catalyst design (composition and architecture) parameters are reported for the conversion of methane and NO x over a dual-layer Pt+Pd/Al 2 O 3 + Mn 0.5 Fe 2.5 O 4 /Al 2 O 3 monolith. CH 4 and NO x conversion data show that the dual-layer catalyst outperforms single-layer samples having the same catalyst loadings, with and without spinel. Close proximity of the PGM and MFO functions in the mixed-layer catalyst lowers the CH 4 conversion at high temperature while separating the PGM and spinel layers with an intermediate Al 2 O 3 layer does not. Methane conversion enhancement is linked to its nonmonotonic dependence on O 2 . The performance gains are tied to a transient activity spike that occurs during the lean-to-rich feed transition when water is present in the feed. The transient spike is attributed to the removal of CO and H 2 products via reactions with stored O 2 in the spinel, eliminating inhibition of methane steam reforming.

03 NATURAL GAS↗

DOE OSTI2027

A multi-region approach for the analysis of porous materials and gas-solid reactions using USAXS-SAXS-WAXS: application to CaO carbonation

The reaction rate of gas-solid non-catalytic reactions is typically investigated using reactant conversion data over time and ex-situ measurements of the porous solid reactant textural properties; these data enable the experimental estimation of the initial intrinsic reaction rate, whereas the evolution of the textural properties and the reaction rate over time are evaluated theoretically using reaction models. In this work, a different methodology is presented, based on: a) in-situ time-resolved USAXS-SAXS-WAXS measurements of the solid sample, and b) a multi-region modeling approach; this methodology allows for the estimation of the textural properties and the intrinsic reaction rate at any time during the reaction. Referring to a reaction in which a solid product is obtained from a solid reactant and a gaseous reactant, the porous particle is described as consisting of several distinct regions with different microstructural properties, and both a gas-solid reaction model and a SAXS model are derived and applied to the carbonation of porous CaO. The proposed reaction model highlights the role of the inaccessible reactant and accurately predicts the solid reactant conversion versus time profile. The SAXS model accurately predicts the measured linear trends of the Porod invariant and of the pre-factor of the power law scattering profile versus CaO mass fraction; in the absence of chemical reactions, the proposed equations extend classical SAXS theory to porous materials containing macropores and nonporous solid phases in addition to standard nanoscale inhomogeneous regions.

CO2 solid sorbents↗

DOE OSTI2023

Assessing the influence of microstructure on uranium hydride size distributions via small angle neutron scattering

Here, the effect of microstructure on the internal hydriding behavior of both cast (1 mm grain size) and rolled (25 μm grain size) uranium containing hydrogen concentrations between 0 and 1.8 wppm were evaluated via small angle neutron scattering (SANS). Increasing hydrogen content up to 1.8 wppm in the cast uranium only weakly affected the average uranium hydride (UH 3 ) precipitate size, calculated from the SANS data. Conversely, the UH 3 phase fraction was found to strongly depend on the hydrogen content in the same cast samples. A substantially reduced UH 3 particle size distribution was observed in the rolled uranium relative to cast uranium containing the same nominal hydrogen content. It is hypothesized that the suppression of UH 3 formation in the rolled uranium is driven by increased hydrogen trapping at grain boundaries, and theoretical calculations that account for trap density, potency, and hydrogen diffusion kinetics support this hypothesis.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Deep Learning for Fish Identification from Sonar Data (CRADA 481 Final Report)

In eastern regions of the United States, the American eel is a species of management and regulatory concern because of significant population declines, despite the species’ previous abundance in all tributaries of rivers flowing into the Atlantic Ocean. The American eel is also a candidate for listing under the U.S. Endangered Species Act. While hydropower construction and operation are only one of several factors contributing to this population decline, such a listing could impose additional regulatory challenges for a large number of hydropower projects. In this CRADA project, we improved technologies for identifying migrating eels with the goal of reducing the cost and time required for future American eel hydropower impact assessment and mitigation studies, while maintaining accuracy. We built on results from a previous FOA project (FOA# DE-FOA-0001662), led by the Electric Power Research Institute (EPRI), which developed a highly accurate, deep-learning method for identifying migrating eels from imaging sonar data. The current study aimed to further optimize this deep-learning model, originally designed for image classification, and to develop an object detection software capable of identifying fish from sonar videos in real time, enabling the detection of events like fish migrations and specific species, such as the American eel, at hydropower dams. The data conversion algorithms were packaged as software with a graphical user interface, and the software is evaluated by external collaborators. We focused on the American eel in this project and explored the transferability of the developed deep learning models to the sea lamprey, given the similar body shape and swimming behavior between the two species.

13 HYDRO ENERGY↗

DOE OSTI2025

Real-Time Wave Energy Converter Control Using Instantaneous Frequency

Wave Energy Converters (WECs) rely on effective Power Take-Off (PTO) control strategies to maximize energy absorption under dynamic sea conditions. Traditional hydrodynamic modeling techniques may require computationally intensive convolution calculations, making real-time control implementation challenging. This paper presents an alternative approach by leveraging instantaneous frequency estimation to dynamically adjust PTO damping in response to varying wave frequencies. Two real-time frequency estimation methods are explored: the Hilbert Transform (HT) and Phase-Locked Loop (PLL). The Hilbert Transform method provides accurate frequency tracking but introduces a delayed response due to its dependence on causal data. Conversely, the PLL approach demonstrates strong potential in frequency tracking but requires careful gain tuning, particularly in complex sea states. Comparative evaluations across multiple test cases—including sinusoidal variations, amplitude steps, frequency step changes, and real-world JONSWAP spectrum waves—highlight the strengths and limitations of each method. The two different PTO control techniques across the various frequency estimation methods were tested under real-sea states using a state-space model of a point-absorbing Wave Energy Converter. The Capture Width Ratio (CWR) is used as a performance metric, with results showing that the HT achieves a 10.6% improvement, while the PLL estimation yields a 0.9% improvement relative to the fixed parameter control baseline. These results highlight the effectiveness of real-time frequency estimation in improving energy absorption compared to static control parameters.

WEC control↗

DOE OSTI2023

In-Pixel Readout IC with compact in-pixel ADC for Pixel detectors at HL LHC

The prototype Smart Pixel concept test chip (SP28), designed in a CMOS 28 nm bulk process, is a proof of concept readout integrated circuit (ROIC) designed for a future Phase III High Luminosity upgrade of the Large Hadron Collider. It employs a synchronous analog to digital converter (ADC) for the front-end design, with signal processing and data conversion within a single bunch crossing of 25 ns. It is therefore capable of accurately detecting hits occurring in consecutive bunch crossings, without off-time registration of events and pileup insensitivity, making it particularly suitable for the inner most layers of the vertex detector. The ROIC consists of a matrix of 32 × 16 pixels, each 25 × 25 µm 2 in size. Each pixel contains a charge sensitive preamplifer with leakage current compensation, three auto-zero comparators for a 2-bit fash-type ADC. The total power consumption is approximately 5 µW per pixel. The measured noise at the output of all the hit comparators across the ROIC is < 90 e- with threshold dispersion < 45 e-RMS, which allows an in-time threshold setting of ≈ 475 e-.

Parpillon, Benjamin↗

DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗

DOE OSTI2022

Hanford Site Composite Analysis Data Package: Exposure Scenarios and Radionuclide Specific Dose Conversion Factors.

This data package summarizes the exposure assumptions, equations, and methods used to calculate radionuclide-specific unit dose factors and the radiological doses for both groundwater and atmospheric pathways as a part of the revised Hanford Site Composite Analysis. An All-Pathways Representative Person exposure scenario is considered to evaluate exposure via both groundwater and atmospheric transport pathways. The radiological dose assessments for both groundwater and atmospheric pathways are included in the performance assessments for various Waste Management Areas at the Hanford Site. This data package calculates exposure route-specific and total unit dose factors for composite-analysis-specific radionuclides of concern based on the exposure assumptions used in the composite analysis and performance assessments. This data package presents the results and comparison of the radionuclide-specific unit dose factors based on the exposure assumptions used in the revised composite analysis and various performance assessments.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2023

Controller area network decoder (CAN-D)

A system and method for decoding an unknown automotive controller area network (“CAN”) message definitions. CAN data vehicle signal mappings are typically held in secret and varied by automotive model and year. Without knowledge of the mappings, the wealth of real-time vehicle data hidden in the automotive CAN packets is uninterpretable—impeding research, after-market tuning, efficiency and performance monitoring, fault diagnosis, and privacy-related technologies. This technology can ascertain the CAN signals' boundaries (start bit and length), endianness (byte ordering), signedness (binary-to-integer encoding) from raw CAN data. This allows conversion of CAN data to time series. Interpreting the translated CAN data's physical meaning and finding a linear mapping to standard units (e.g., knowing the signal is speed and scaling values to represent units of miles per hour) can be achieved for many signals by leveraging diagnostic standards to obtain real-time measurements of in-vehicle systems. The system and method can be integrated into lightweight hardware enabling an OBD-II plugin for real-time in-vehicle CAN decoding or run on standard computers. The system can output a standard DBC file with the signal definition information.

Verma, Kiren E.↗